






import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_MAPPING,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitModel,
  )
  from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  import PIL
  from PIL import Image

  from transformers import BeitImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			int       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Any=100       ,				lowerCamelCase		:			Tuple=13       ,				lowerCamelCase		:			Dict=30       ,				lowerCamelCase		:			Dict=2       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			str=True       ,				lowerCamelCase		:			str=True       ,				lowerCamelCase		:			Optional[Any]=32       ,				lowerCamelCase		:			Dict=4       ,				lowerCamelCase		:			List[Any]=4       ,				lowerCamelCase		:			Dict=37       ,				lowerCamelCase		:			Union[str, Any]="gelu"       ,				lowerCamelCase		:			int=0.1       ,				lowerCamelCase		:			Tuple=0.1       ,				lowerCamelCase		:			Any=10       ,				lowerCamelCase		:			List[str]=0.02       ,				lowerCamelCase		:			Optional[Any]=3       ,				lowerCamelCase		:			Optional[int]=None       ,				lowerCamelCase		:			int=[0, 1, 2, 3]       ,				)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    100
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    patch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    type_sequence_label_size
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    scope
         _UpperCAmelCase							=    out_indices
         _UpperCAmelCase							=    num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         _UpperCAmelCase							=    (image_size // patch_size) ** 2
         _UpperCAmelCase							=    num_patches + 1


  def  lowerCamelCase       (      self		:			Tuple							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.image_size, self.image_size]       ,				self.num_labels							)

         _UpperCAmelCase							=    self.get_config()

         return config, pixel_values, labels, pixel_labels


  def  lowerCamelCase       (      self		:			int							)						->	str:


         """simple docstring"""
         return BeitConfig(
             vocab_size=self.vocab_size       ,				image_size=self.image_size       ,				patch_size=self.patch_size       ,				num_channels=self.num_channels       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				out_indices=self.out_indices       ,				)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[Any]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    BeitModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			int							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    BeitForMaskedImageModeling(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length - 1, self.vocab_size)							)


  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Tuple							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.type_sequence_label_size
         _UpperCAmelCase							=    BeitForImageClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)							)

         # test greyscale images
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    BeitForImageClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)							)


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Any							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_labels
         _UpperCAmelCase							=    BeitForSemanticSegmentation(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(
             result.logits.shape       ,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)							)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(
             result.logits.shape       ,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)							)





  def  lowerCamelCase       (      self		:			int							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    config_and_inputs
         _UpperCAmelCase							=    {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      {
          '''feature-extraction''': BeitModel,
          '''image-classification''': BeitForImageClassification,
          '''image-segmentation''': BeitForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			str							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    BeitModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         self.config_tester.run_common_tests()


  @unittest.skip(reason="""BEiT does not use inputs_embeds"""							)
  def  lowerCamelCase       (      self		:			str							)						->	Optional[Any]:


         """simple docstring"""
         pass


  @require_torch_multi_gpu
  @unittest.skip(reason="""BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""							)
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[int]:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                self.assertIsInstance(model.get_input_embeddings()       ,				(nn.Module)							)
                _UpperCAmelCase							=    model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(lowerCamelCase       ,				nn.Linear							)							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                _UpperCAmelCase							=    inspect.signature(model.forward							)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _UpperCAmelCase							=    [*signature.parameters.keys()]

                _UpperCAmelCase							=    ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	str:


         """simple docstring"""
         if not self.model_tester.is_training:
                return

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							=    True

         for model_class in self.all_model_classes:
                # we don't test BeitForMaskedImageModeling
                if model_class in [*get_values(lowerCamelCase							), BeitForMaskedImageModeling]:
                       continue

                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.train()
                _UpperCAmelCase							=    self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)
                _UpperCAmelCase							=    model(**lowerCamelCase							).loss
                loss.backward()


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
                return

         _UpperCAmelCase							=    False
         _UpperCAmelCase							=    True

         for model_class in self.all_model_classes:
                # we don't test BeitForMaskedImageModeling
                if (
                    model_class in [*get_values(lowerCamelCase							), BeitForMaskedImageModeling]
                    or not model_class.supports_gradient_checkpointing
                ):
                       continue

                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.gradient_checkpointing_enable()
                model.to(lowerCamelCase							)
                model.train()
                _UpperCAmelCase							=    self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)
                _UpperCAmelCase							=    model(**lowerCamelCase							).loss
                loss.backward()


  def  lowerCamelCase       (      self		:			Dict							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         _UpperCAmelCase							=    _config_zero_init(lowerCamelCase							)
         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    model_class(config=lowerCamelCase							)
                for name, param in model.named_parameters():
                       # we skip lambda parameters as these require special initial values
                       # determined by config.layer_scale_init_value
                       if "lambda" in name:
                              continue
                       if param.requires_grad:
                              self.assertIn(
                                  ((param.data.mean() * 1E9).round() / 1E9).item()       ,				[0.0, 1.0]       ,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""       ,				)





  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	int:


         """simple docstring"""
         for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    BeitModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						str:
       _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       return image





@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			str							)						->	List[str]:


         """simple docstring"""
         return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""							) if is_vision_available() else None


  @slow
  def  lowerCamelCase       (      self		:			List[str]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""							).to(lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).pixel_values.to(lowerCamelCase							)

         # prepare bool_masked_pos
         _UpperCAmelCase							=    torch.ones((1, 196)       ,				dtype=torch.bool							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(pixel_values=lowerCamelCase       ,				bool_masked_pos=lowerCamelCase							)
         _UpperCAmelCase							=    outputs.logits

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 196, 8192)							)
         self.assertEqual(logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor(
             [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]							).to(lowerCamelCase							)

         self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]       ,				lowerCamelCase       ,				atol=1E-2							)							)


  @slow
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""							).to(lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(**lowerCamelCase							)
         _UpperCAmelCase							=    outputs.logits

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 1000)							)
         self.assertEqual(logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([-1.2385, -1.0987, -1.0108]							).to(lowerCamelCase							)

         self.assertTrue(torch.allclose(logits[0, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)

         _UpperCAmelCase							=    281
         self.assertEqual(logits.argmax(-1							).item()       ,				lowerCamelCase							)


  @slow
  def  lowerCamelCase       (      self		:			str							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""							).to(
             lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(**lowerCamelCase							)
         _UpperCAmelCase							=    outputs.logits

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 2_1841)							)
         self.assertEqual(logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([1.6881, -0.2787, 0.5901]							).to(lowerCamelCase							)

         self.assertTrue(torch.allclose(logits[0, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)

         _UpperCAmelCase							=    2396
         self.assertEqual(logits.argmax(-1							).item()       ,				lowerCamelCase							)


  @slow
  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""							)
         _UpperCAmelCase							=    model.to(lowerCamelCase							)

         _UpperCAmelCase							=    BeitImageProcessor(do_resize=lowerCamelCase       ,				size=640       ,				do_center_crop=lowerCamelCase							)

         _UpperCAmelCase							=    load_dataset("""hf-internal-testing/fixtures_ade20k"""       ,				split="""test"""							)
         _UpperCAmelCase							=    Image.open(ds[0]["""file"""]							)
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(**lowerCamelCase							)
         _UpperCAmelCase							=    outputs.logits

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 150, 160, 160)							)
         self.assertEqual(logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    version.parse(PIL.__version__							) < version.parse("""9.0.0"""							)

         if is_pillow_less_than_a:
                _UpperCAmelCase							=    torch.tensor(
                    [
                        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
                    ]       ,				device=lowerCamelCase       ,				)
         else:
                _UpperCAmelCase							=    torch.tensor(
                    [
                        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
                        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
                        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
                    ]       ,				device=lowerCamelCase       ,				)

         self.assertTrue(torch.allclose(logits[0, :3, :3, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""							)
         _UpperCAmelCase							=    model.to(lowerCamelCase							)

         _UpperCAmelCase							=    BeitImageProcessor(do_resize=lowerCamelCase       ,				size=640       ,				do_center_crop=lowerCamelCase							)

         _UpperCAmelCase							=    load_dataset("""hf-internal-testing/fixtures_ade20k"""       ,				split="""test"""							)
         _UpperCAmelCase							=    Image.open(ds[0]["""file"""]							)
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(**lowerCamelCase							)

         _UpperCAmelCase							=    outputs.logits.detach().cpu()

         _UpperCAmelCase							=    image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase       ,				target_sizes=[(500, 300)]							)
         _UpperCAmelCase							=    torch.Size((500, 300)							)
         self.assertEqual(segmentation[0].shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase							)
         _UpperCAmelCase							=    torch.Size((160, 160)							)
         self.assertEqual(segmentation[0].shape       ,				lowerCamelCase							)
import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   UNetaDModel							, lowercase_				:   UNetaDModel							, lowercase_				:   DDPMScheduler							, lowercase_				:   Any							, )						->	Any:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    value_function
   _UpperCamelCase           =    unet
   _UpperCamelCase           =    scheduler
   _UpperCamelCase           =    env
   _UpperCamelCase           =    env.get_dataset()
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].mean()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].std()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    env.observation_space.shape[0]
   _UpperCamelCase           =    env.action_space.shape[0]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[int]							, lowercase_				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   return (x_in - self.means[key]) / self.stds[key]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   return x_in * self.stds[key] + self.means[key]







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   if type(lowercase_) is dict:
     return {k: self.to_torch(lowercase_) for k, v in x_in.items()}
   elif torch.is_tensor(lowercase_):
     return x_in.to(self.unet.device)
   return torch.tensor(lowercase_							, device=self.unet.device)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Any							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[str])						->	Tuple:


   """simple docstring"""







   for key, val in cond.items():
     _UpperCamelCase           =    val.clone()
   return x_in







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   List[Any]							, lowercase_				:   int)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    x.shape[0]
   _UpperCamelCase           =    None
   for i in tqdm.tqdm(self.scheduler.timesteps):
     # create batch of timesteps to pass into model
     _UpperCamelCase           =    torch.full((batch_size,)							, lowercase_							, device=self.unet.device							, dtype=torch.long)
     for _ in range(lowercase_):
       with torch.enable_grad():
         x.requires_grad_()

         # permute to match dimension for pre-trained models
         _UpperCamelCase           =    self.value_function(x.permute(0							, 2							, 1)							, lowercase_).sample
         _UpperCamelCase           =    torch.autograd.grad([y.sum()]							, [x])[0]

         _UpperCamelCase           =    self.scheduler._get_variance(lowercase_)
         _UpperCamelCase           =    torch.exp(0.5 * posterior_variance)
         _UpperCamelCase           =    model_std * grad

       _UpperCamelCase           =    0
       _UpperCamelCase           =    x.detach()
       _UpperCamelCase           =    x + scale * grad
       _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)

     _UpperCamelCase           =    self.unet(x.permute(0							, 2							, 1)							, lowercase_).sample.permute(0							, 2							, 1)

     # TODO: verify deprecation of this kwarg
     _UpperCamelCase           =    self.scheduler.step(lowercase_							, lowercase_							, lowercase_							, predict_epsilon=lowercase_)["prev_sample"]

     # apply conditions to the trajectory (set the initial state)
     _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
     _UpperCamelCase           =    self.to_torch(lowercase_)
   return x, y







 def __call__(		self				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   int=64							, lowercase_				:   Any=32							, lowercase_				:   List[Any]=2							, lowercase_				:   str=0.1)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.normalize(lowercase_							, "observations")
   _UpperCamelCase           =    obs[None].repeat(lowercase_							, axis=0)

   _UpperCamelCase           =    {0: self.to_torch(lowercase_)}
   _UpperCamelCase           =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

   # generate initial noise and apply our conditions (to make the trajectories start at current state)
   _UpperCamelCase           =    randn_tensor(lowercase_							, device=self.unet.device)
   _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
   _UpperCamelCase           =    self.to_torch(lowercase_)

   # run the diffusion process
   _UpperCamelCase      ,						_UpperCamelCase           =    self.run_diffusion(lowercase_							, lowercase_							, lowercase_							, lowercase_)

   # sort output trajectories by value
   _UpperCamelCase           =    y.argsort(0							, descending=lowercase_).squeeze()
   _UpperCamelCase           =    x[sorted_idx]
   _UpperCamelCase           =    sorted_values[:, :, : self.action_dim]
   _UpperCamelCase           =    actions.detach().cpu().numpy()
   _UpperCamelCase           =    self.de_normalize(lowercase_							, key="actions")

   # select the action with the highest value
   if y is not None:
     _UpperCamelCase           =    0
   else:
     # if we didn't run value guiding, select a random action
     _UpperCamelCase           =    np.random.randint(0							, lowercase_)

   _UpperCamelCase           =    denorm_actions[selected_index, 0]
   return denorm_actions




